FMBN statistics.

This report is designed to provide summary statistics for FoodMicrobionet, version 5.0 or higher. It takes as an input the FMBN_plus list and returns text, tables and graphs.
These results are for FoodMicrobionet version .

Statistics on studies.

The number of studies in FMBN is 251. This version includes studies on fungal microbiota only (11), on bacterial microbiota only (230) and studies for which both data for bacterial and fungal microbiota are available. However, due to inconsistencies in the deposit of sequences in SRA (in several cases the same sample was deposited with two separate biosample accessions and/or data for bacteria and fungi were deposited with different bioproject of study accessions), the same samples might be present in two studies, one for bacteria and one for fungi1. We did our best to match samples in these situations. The addition of datasets on fungi is in progress and, when available, we will progressively add fungal data for all the studies which are already in FoodMicrobionet with bacterial community data and add more fungal studies.

## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_align()`).

The largest growth in studies and samples has been between version 3.1 (published in 2019) and version 3.2 (unpublished). Note that some older studies were annotated as belonging to version 5.0 when fungal data were added.

Platforms, gene target, regions.

FMBN grows by addition of sequences deposited in NCBI SRA for published studies. As a consequence, use of targets (16S RNA, 16S RNA gene, ITS) reflect what is published and the correlation between platforms and targets.

## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `year = as.numeric(year)`.
## Caused by warning:
## ! NAs introduced by coercion
## Joining with `by = join_by(year, platform_2)`

Recently, the number of studies using Illumina newer platforms (other than MiSeq) is growing quickly.

The regios used as targets also reflects the use of different platforms and the preference for a given target (V4 and V3-V4), at least for bacteria. Data for fungi are still not sufficient to define a trend.
The following table reports the number of studies available for each platform and the croos-tabulation of studies by gene target and platform.

##                 platform_2
## region           454 GS Illumina Ion Torrent Sum
##   ITS1                0        6           0   6
##   ITS1 and V3-V4      0        8           0   8
##   ITS1 and V4         0        1           0   1
##   ITS1+ITS2           1        0           0   1
##   ITS2                0        4           0   4
##   ITS2 and V3-V4      0        1           0   1
##   V1-V2               1        2           2   5
##   V1-V3              33       13           0  46
##   V2-V3               1        0           0   1
##   V3                  1        3           0   4
##   V3-V4               1      122           0 123
##   V4                  2       33           3  38
##   V4-V5               2        5           0   7
##   V4-V6               0        1           0   1
##   V5                  1        0           0   1
##   V5-V6               0        1           1   2
##   V5-V9               1        0           0   1
##   V6-V8               0        1           0   1
##   Sum                44      201           6 251

A large number of studies targets V3-V4 or V4 for bacteria and ITS1 for fungi.

## Joining with `by = join_by(year, region)`

The distribution of studies by platform and region reflect current practices in metataxonomic analysis of food microbial communities. With phasing out of 454 GS (most studies targeted V1-V3), the majority of studies for bacteria is now Illumina with either V3-V4 (which alone make 55.23% of studies) or V1-V3 (18.83%).

Data for fungi (21 studies only) are summarized below:

## Joining with `by = join_by(year, region)`

However, data for fungi in FoodMicrobionet are still too scarse to draw any conclusion of target preferences.

The majority of studies is on dairy products.

Statistics on samples.

The number of samples in FMBN has been rising almost exponentially with time and it is now 1.4976^{4}. There are 14035 samples with data on bacteria, 1114 samples with data on fungi. For 366 samples we were able to match samples for bacteria and fungi.

FMBN is the richest database in terms of number of samples for foods and food environments, and it is also the best annotated one.

Statistics by food group.

A few statistics on samples are shown below. The following table shows the proportion and cumulative proportion of unique food samples and food environment samples, classified by the L1 level of the FoodEx2 classification. Mock communities and extraction blanks are included.

L1 n prop cumprop
Milk and dairy products 5807 0.3931 0.3931
Meat and meat products 3605 0.2440 0.6371
Vegetables and vegetable products 1508 0.1021 0.7391
Fruit and fruit products 1178 0.0797 0.8189
Fish, seafood, amphibians, reptiles and invertebrates 814 0.0551 0.8740
Alcoholic beverages 513 0.0347 0.9087
Major isolated ingredients, additives, flavours, baking and processing aids 339 0.0229 0.9316
Seasoning, sauces and condiments 202 0.0137 0.9453
Grains and grain-based products 155 0.0105 0.9558
Composite dishes 147 0.0099 0.9658
Fruit and vegetable juices and nectars (including concentrates) 82 0.0056 0.9713
Legumes, nuts, oilseeds and spices 81 0.0055 0.9768
Generic food environments 64 0.0043 0.9811
Food products for young population 61 0.0041 0.9852
Eggs and egg products 56 0.0038 0.9890
Animal and vegetable fats and oils and primary derivatives thereof 53 0.0036 0.9926
Sugar and similar, confectionery and water-based sweet desserts 42 0.0028 0.9955
Extraction blank 25 0.0017 0.9972
Starchy roots or tubers and products thereof, sugar plants 17 0.0012 0.9983
Coffee, cocoa, tea and infusions 15 0.0010 0.9993
Mock community 10 0.0007 1.0000

Samples in FMBN belong to 21 major food groups (L1 level of FoodEx2 exposure classification).
There are 2634 environmental samples and 12125 food samples. Samples in FMBN are further classified using levels L4 and L6 of the FoodEx2 exposure classification, and additional fields (which allow to identify raw products, intermediates or finished products, the level of thermal treatment and the occurrence of spoilage and/or fermentation) allow a finer classification. Samples in FMBN belong to 134 L4 food groups and 239 L6 food groups. There are 199 foodIds (food types), and, combining further information on samples (nature, heat treatment, spoilage/fermentation), there are 388 combinations.

Statistics by gene targets and number of sequences.

The structure of FoodMicrobionet allows the user to fine-tune each search and extract just the combination of samples s/he desires. Below, I am showing a few stats on number of sequences, by region. However, the user can perform searches based on the type of target, the region, he length of sequences per sample and even the occurrence of issues during the bioinformatic analysis (low number of sequences, high proportion of losses in a specific phase of the pipeline).

Geographical distribution of samples.

From version 4.1.2 geographic location of samples (when provided in metadata) was added to the samples table.
We plan to fill up this information on existing samples and will continue adding it to new samples. However, interested users should always double check on the original paper for the meaning of the coordinates (are they the place of sampling? the origin of the food? For example, there is a Japanese study studying imported French cheeses: which should be the location?).

geo_loc_continent n prop cumprop
Europe 8137 0.5508 0.5508
North America 2515 0.1702 0.7210
Asia 2212 0.1497 0.8707
Oceania 901 0.0610 0.9317
Africa 520 0.0352 0.9669
South America 316 0.0214 0.9883
Belgium 104 0.0070 0.9953
NA 69 0.0047 1.0000
geo_loc_country n prop cumprop
Italy 3296 0.2231 0.2231
United States of America 1795 0.1215 0.3446
France 1423 0.0963 0.4409
Norway 941 0.0637 0.5046
Australia 893 0.0604 0.5650
China 879 0.0595 0.6245
South Korea 660 0.0447 0.6692
Canada 600 0.0406 0.7098
Ireland 599 0.0405 0.7504
United Kingdom 371 0.0251 0.7755
Sweden 355 0.0240 0.7995
Belgium 285 0.0193 0.8188
Cyprus 251 0.0170 0.8358
Brazil 217 0.0147 0.8505
Spain 186 0.0126 0.8631
Senegal 120 0.0081 0.8712
Ivory Coast 119 0.0081 0.8792
Europe 104 0.0070 0.8863
NA 103 0.0070 0.8933
Japan 101 0.0068 0.9001
Finland 96 0.0065 0.9066
Austria 91 0.0062 0.9128
Thailand 84 0.0057 0.9184
Greenland 75 0.0051 0.9235
Benin 66 0.0045 0.9280
Netherlands 63 0.0043 0.9322
Malaysia 61 0.0041 0.9364
Portugal 59 0.0040 0.9404
Cameroon 58 0.0039 0.9443
Germany 58 0.0039 0.9482
Israel 56 0.0038 0.9520
Denmark 46 0.0031 0.9551
Mexico 45 0.0030 0.9582
South Africa 42 0.0028 0.9610
Colombia 40 0.0027 0.9637
Laos 40 0.0027 0.9664
Switzerland 36 0.0024 0.9689
Estonia 32 0.0022 0.9710
Pakistan 31 0.0021 0.9731
Croatia 28 0.0019 0.9750
Brasil 26 0.0018 0.9768
Ethiopia 23 0.0016 0.9783
Hungary 23 0.0016 0.9799
Iceland 20 0.0014 0.9813
Russia 20 0.0014 0.9826
Argentina 17 0.0012 0.9838
Poland 16 0.0011 0.9848
Bosnia-Erzegovina 15 0.0010 0.9859
Iran 15 0.0010 0.9869
Greece 14 0.0009 0.9878
Madagascar 14 0.0009 0.9888
Kazakhstan 13 0.0009 0.9896
Zambia 13 0.0009 0.9905
Gabon 12 0.0008 0.9913
Serbia 11 0.0007 0.9921
Ghana 10 0.0007 0.9928
Guyana 10 0.0007 0.9934
Maldives 10 0.0007 0.9941
Nigeria 8 0.0005 0.9947
Papua New Guinea 8 0.0005 0.9952
Georgia 7 0.0005 0.9957
Chile 6 0.0004 0.9961
Guinea 6 0.0004 0.9965
Morocco 6 0.0004 0.9969
Bulgaria 5 0.0003 0.9972
Great Britain 5 0.0003 0.9976
Zimbabwe 5 0.0003 0.9979
Rwanda 4 0.0003 0.9982
Svalbard 4 0.0003 0.9984
Uganda 4 0.0003 0.9987
Burkina Faso 3 0.0002 0.9989
Tanzania 3 0.0002 0.9991
Czech Republic 2 0.0001 0.9993
Indonesia 2 0.0001 0.9994
Kenya 2 0.0001 0.9995
Lithuania 2 0.0001 0.9997
Namibia 2 0.0001 0.9998
Viet Nam 2 0.0001 0.9999
Latvia 1 0.0001 1.0000
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These statistics can be easily calculated separately for bacteria and fungi.

Statistics for taxa.

For all studies belonging to version 1.1 or higher, FoodMicrobionet was created by a dedicated pipeline using SILVA for taxonomic assignment; from version 5.0 UNITE was used as a taxonomic reference for ITS sequences for fungi. A few tweaks on taxonomy are needed for coherence and for compatibility with external databases.

We always try to assign sequences to the lowest possible level (given the length of sequences and their quality). Statistics for taxonomic assignment are shown below.

Taxonomic assignment, by taxonomic level, bacteria
idelevel n prop cumprop
species 5147 0.524 0.524
genus 3361 0.342 0.867
family 648 0.066 0.933
order 364 0.037 0.970
class 223 0.023 0.993
phylum 69 0.007 1.000
domain 2 0.000 1.000
Taxonomic assignment, by taxonomic level, fungi
idelevel n prop cumprop
species 2076 0.735 0.735
genus 612 0.217 0.952
family 91 0.032 0.984
order 31 0.011 0.995
class 11 0.004 0.999
phylum 3 0.001 1.000
domain 1 0.000 1.000

There are currently 1.2782^{4} taxa in this version of FoodMicrobionet, identified at different identification levels. The proportion of taxa identified at the genus level or below is 0.867 for bacteria and 0.952 for fungi.

Proportion by phyla, Bacteria and Archaea
phylum n prop cumprop cumn
Proteobacteria 3257 0.3319 0.3319 3257
Firmicutes 2089 0.2129 0.5447 5346
Actinobacteriota 1366 0.1392 0.6839 6712
Bacteroidota 1330 0.1355 0.8194 8042
Cyanobacteria 255 0.0260 0.8454 8297
Verrucomicrobiota 149 0.0152 0.8606 8446
Desulfobacterota 136 0.0139 0.8745 8582
Acidobacteriota 134 0.0137 0.8881 8716
Planctomycetota 122 0.0124 0.9006 8838
Chloroflexi 121 0.0123 0.9129 8959
Patescibacteria 107 0.0109 0.9238 9066
Campylobacterota 76 0.0077 0.9315 9142
Myxococcota 73 0.0074 0.9390 9215
Halobacterota 60 0.0061 0.9451 9275
Spirochaetota 56 0.0057 0.9508 9331
Deinococcota 54 0.0055 0.9563 9385
Fusobacteriota 36 0.0037 0.9600 9421
Synergistota 34 0.0035 0.9634 9455
Bdellovibrionota 24 0.0024 0.9659 9479
Crenarchaeota 21 0.0021 0.9680 9500
Fibrobacterota 19 0.0019 0.9699 9519
Elusimicrobiota 17 0.0017 0.9717 9536
Gemmatimonadota 17 0.0017 0.9734 9553
Armatimonadota 15 0.0015 0.9749 9568
Cloacimonadota 14 0.0014 0.9764 9582
Halanaerobiaeota 14 0.0014 0.9778 9596
Nitrospirota 13 0.0013 0.9791 9609
Thermoplasmatota 13 0.0013 0.9804 9622
Tenericutes 11 0.0011 0.9816 9633
Thermotogota 11 0.0011 0.9827 9644
Euryarchaeota 9 0.0009 0.9836 9653
Methylomirabilota 9 0.0009 0.9845 9662
Deferribacterota 7 0.0007 0.9852 9669
Dependentiae 7 0.0007 0.9859 9676
Nitrospinota 5 0.0005 0.9864 9681
TM7 5 0.0005 0.9870 9686
Chlorobi 4 0.0004 0.9874 9690
GN02 4 0.0004 0.9878 9694
Latescibacterota 4 0.0004 0.9882 9698
Nanoarchaeota 4 0.0004 0.9886 9702
Aquificota 3 0.0003 0.9889 9705
Calditrichota 3 0.0003 0.9892 9708
Chlamydiae 3 0.0003 0.9895 9711
Dictyoglomota 3 0.0003 0.9898 9714
OP3 3 0.0003 0.9901 9717
AD3 2 0.0002 0.9903 9719
Abditibacteriota 2 0.0002 0.9905 9721
BRC1 2 0.0002 0.9907 9723
Caldatribacteriota 2 0.0002 0.9909 9725
Caldisericota 2 0.0002 0.9911 9727
Coprothermobacterota 2 0.0002 0.9913 9729
Entotheonellaeota 2 0.0002 0.9915 9731
Hydrogenedentes 2 0.0002 0.9917 9733
Lentisphaerae 2 0.0002 0.9920 9735
Modulibacteria 2 0.0002 0.9922 9737
Nanoarchaeaeota 2 0.0002 0.9924 9739
SBR1093 2 0.0002 0.9926 9741
Spirochaetes 2 0.0002 0.9928 9743
Sumerlaeota 2 0.0002 0.9930 9745
Thermodesulfobiota 2 0.0002 0.9932 9747
WS3 2 0.0002 0.9934 9749
NA 2 0.0002 0.9936 9751
ABY1_OD1 1 0.0001 0.9937 9752
Acetothermia 1 0.0001 0.9938 9753
Aegiribacteria 1 0.0001 0.9939 9754
Aenigmarchaeota 1 0.0001 0.9940 9755
Altiarchaeota 1 0.0001 0.9941 9756
Atribacteria 1 0.0001 0.9942 9757
CCM11b 1 0.0001 0.9943 9758
Candidatus Tectomicrobia 1 0.0001 0.9944 9759
Chlorophyta 1 0.0001 0.9945 9760
DTB120 1 0.0001 0.9946 9761
Dadabacteria 1 0.0001 0.9947 9762
Deferrisomatota 1 0.0001 0.9948 9763
Elusimicrobia 1 0.0001 0.9949 9764
FBP 1 0.0001 0.9950 9765
FCPU426 1 0.0001 0.9951 9766
FW113 1 0.0001 0.9952 9767
Fermentibacterota 1 0.0001 0.9953 9768
Firestonebacteria 1 0.0001 0.9954 9769
GAL15 1 0.0001 0.9955 9770
GN06 1 0.0001 0.9956 9771
GOUTA4 1 0.0001 0.9957 9772
Hydrothermae 1 0.0001 0.9958 9773
Iainarchaeota 1 0.0001 0.9959 9774
LCP-89 1 0.0001 0.9960 9775
Latescibacteria 1 0.0001 0.9961 9776
MBNT15 1 0.0001 0.9962 9777
Margulisbacteria 1 0.0001 0.9963 9778
Marinimicrobia (SAR406 clade) 1 0.0001 0.9964 9779
Marinimicrobia_(SAR406_clade) 1 0.0001 0.9965 9780
Micrarchaeota 1 0.0001 0.9966 9781
NB1-j 1 0.0001 0.9967 9782
NKB15 1 0.0001 0.9968 9783
NKB19 1 0.0001 0.9969 9784
Nanohaloarchaeota 1 0.0001 0.9970 9785
OD1 1 0.0001 0.9971 9786
OP11 1 0.0001 0.9972 9787
OP8 1 0.0001 0.9974 9788
Omnitrophicaeota 1 0.0001 0.9975 9789
PAUC34f 1 0.0001 0.9976 9790
Poribacteria 1 0.0001 0.9977 9791
RCP2-54 1 0.0001 0.9978 9792
Rs-K70 termite group 1 0.0001 0.9979 9793
SAR324 clade(Marine group B) 1 0.0001 0.9980 9794
SAR324_clade(Marine_group_B) 1 0.0001 0.9981 9795
SC4 1 0.0001 0.9982 9796
SM2F11 1 0.0001 0.9983 9797
SPAM 1 0.0001 0.9984 9798
SR1 1 0.0001 0.9985 9799
Schekmanbacteria 1 0.0001 0.9986 9800
Sva0485 1 0.0001 0.9987 9801
TA06 1 0.0001 0.9988 9802
TM6 1 0.0001 0.9989 9803
TX1A-33 1 0.0001 0.9990 9804
Thaumarchaeota 1 0.0001 0.9991 9805
Thermodesulfobacteria 1 0.0001 0.9992 9806
Thermotogae 1 0.0001 0.9993 9807
WOR-1 1 0.0001 0.9994 9808
WPS-2 1 0.0001 0.9995 9809
WS1 1 0.0001 0.9996 9810
WS2 1 0.0001 0.9997 9811
WS4 1 0.0001 0.9998 9812
ZB2 1 0.0001 0.9999 9813
Zixibacteria 1 0.0001 1.0000 9814
Proportion by phyla, Fungi
phylum n prop cumprop cumn
Ascomycota 1677 0.5938 0.5938 1677
Basidiomycota 1084 0.3839 0.9777 2761
Mucoromycota 22 0.0078 0.9855 2783
Mortierellomycota 19 0.0067 0.9922 2802
Chytridiomycota 9 0.0032 0.9954 2811
Olpidiomycota 4 0.0014 0.9968 2815
Glomeromycota 3 0.0011 0.9979 2818
Aphelidiomycota 2 0.0007 0.9986 2820
Blastocladiomycota 1 0.0004 0.9989 2821
Fungi_phy_Incertae_sedis 1 0.0004 0.9993 2822
Rozellomycota 1 0.0004 0.9996 2823
Zoopagomycota 1 0.0004 1.0000 2824

The variety of taxa detected is very high, especially for Bacteria and Archaea. There are 124 different bacterial phyla in this version of FoodMicrobionet.

More on taxonomic assignment.

The depth of taxonomic assignment depends on a number of factors (quality and length of the sequences, quality of the reference database, etc.). Here, we will present tables and graphs on this subject, for bacteria only (UNITE results far more often in taxonomic assignments at the species level, even for short sequences).

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## Joining with `by = join_by(taxonId)`
## Warning in left_join(edges_sel_ann, select(taxa, taxonId:species, idelevel)): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 896134 of `x` matches multiple rows in `y`.
## ℹ Row 678 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.
## Joining with `by = join_by(studyId)`
## Joining with `by = join_by(studyId)`
## Joining with `by = join_by(studyId, idelevel)`
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After some processing to obtain the information from the various tables, here is a box plot showing identifications at the genus level or below, by region, for bacteria only.

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## Joining with `by = join_by(studyId)`

Longer sequences for which a good overlap was obtained in paired end sequences (_TRUE) clearly result in a higher proportion of taxonomic assignments at the genus level or below. There is some relationship with the quality of sequences /number of issues encountered during bioinformatic processing: with more issues, in general, the quality of taxonomic assignment is worse, but this is not always true.
However, if one keeps into account the number of sequences rather than just counting the ASVs for which assignment at the genus level or below level was possible, it is clear that a high proportion of total sequences received taxonomic assignment at the genus level or below.

It is very likely that this situation may depend on biases in the composition of reference taxonomic databases (in this case SILVA v138.1), in which the number of sequences varies widely in different taxonomic groups.

The ability to obtain a taxonomic assignment down to the genus level varies by phylum. Only the 4 most abundant phyla are shown.

## Joining with `by = join_by(studyId, phylum, idelevel)`
## `summarise()` has grouped output by 'studyId'. You can override using the
## `.groups` argument.
## Joining with `by = join_by(studyId)`

This is even more evident if the data are weighted using the number of sequences and if only the most common target regions are used.

Taxonomic assignment down to the genus level is clearly worse for Actinobacterota and Bacteroidota and region V1-V3 tipycally results in a higher proportion of taxonomic assignments down to the genus level. However, this is likely to be confounded with the taxonomic platform.

Credits and copyright.

This is version 3.0 of the script, 3/4/2024.

Assume that the code in this document is overall under MIT licence

Copyright 2018, 2019, 2020, 2022, 2023, 2024 Eugenio Parente, Università della Basilicata; version 5 of FoodMicrobionet was created within the PRIN 2022 project NYCdiversity P20229JMMH, and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4 – D.D. 1032 17/06/2022).

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Citations for R packages used in this document.

## $tidyverse
## To cite package 'tidyverse' in publications use:
## 
##   Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R,
##   Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller
##   E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V,
##   Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). "Welcome to
##   the tidyverse." _Journal of Open Source Software_, *4*(43), 1686.
##   doi:10.21105/joss.01686 <https://doi.org/10.21105/joss.01686>.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Article{,
##     title = {Welcome to the {tidyverse}},
##     author = {Hadley Wickham and Mara Averick and Jennifer Bryan and Winston Chang and Lucy D'Agostino McGowan and Romain François and Garrett Grolemund and Alex Hayes and Lionel Henry and Jim Hester and Max Kuhn and Thomas Lin Pedersen and Evan Miller and Stephan Milton Bache and Kirill Müller and Jeroen Ooms and David Robinson and Dana Paige Seidel and Vitalie Spinu and Kohske Takahashi and Davis Vaughan and Claus Wilke and Kara Woo and Hiroaki Yutani},
##     year = {2019},
##     journal = {Journal of Open Source Software},
##     volume = {4},
##     number = {43},
##     pages = {1686},
##     doi = {10.21105/joss.01686},
##   }
## 
## $randomcoloR
## To cite package 'randomcoloR' in publications use:
## 
##   Ammar R (2019). _randomcoloR: Generate Attractive Random Colors_. R
##   package version 1.1.0.1,
##   <https://CRAN.R-project.org/package=randomcoloR>.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {randomcoloR: Generate Attractive Random Colors},
##     author = {Ron Ammar},
##     year = {2019},
##     note = {R package version 1.1.0.1},
##     url = {https://CRAN.R-project.org/package=randomcoloR},
##   }
## 
## ATTENTION: This citation information has been auto-generated from the
## package DESCRIPTION file and may need manual editing, see
## 'help("citation")'.
## 
## $knitr
## To cite package 'knitr' in publications use:
## 
##   Xie Y (2023). _knitr: A General-Purpose Package for Dynamic Report
##   Generation in R_. R package version 1.45, <https://yihui.org/knitr/>.
## 
##   Yihui Xie (2015) Dynamic Documents with R and knitr. 2nd edition.
##   Chapman and Hall/CRC. ISBN 978-1498716963
## 
##   Yihui Xie (2014) knitr: A Comprehensive Tool for Reproducible
##   Research in R. In Victoria Stodden, Friedrich Leisch and Roger D.
##   Peng, editors, Implementing Reproducible Computational Research.
##   Chapman and Hall/CRC. ISBN 978-1466561595
## 
## To see these entries in BibTeX format, use 'print(<citation>,
## bibtex=TRUE)', 'toBibtex(.)', or set
## 'options(citation.bibtex.max=999)'.
## 
## $rnaturalearth
## To cite package 'rnaturalearth' in publications use:
## 
##   Massicotte P, South A (2023). _rnaturalearth: World Map Data from
##   Natural Earth_. R package version 1.0.1,
##   <https://CRAN.R-project.org/package=rnaturalearth>.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {rnaturalearth: World Map Data from Natural Earth},
##     author = {Philippe Massicotte and Andy South},
##     year = {2023},
##     note = {R package version 1.0.1},
##     url = {https://CRAN.R-project.org/package=rnaturalearth},
##   }
## 
## $rnaturalearthdata
## To cite package 'rnaturalearthdata' in publications use:
## 
##   South A, Michael S, Massicotte P (2024). _rnaturalearthdata: World
##   Vector Map Data from Natural Earth Used in 'rnaturalearth'_. R
##   package version 1.0.0,
##   <https://CRAN.R-project.org/package=rnaturalearthdata>.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {rnaturalearthdata: World Vector Map Data from Natural Earth Used in 'rnaturalearth'},
##     author = {Andy South and Schramm Michael and Philippe Massicotte},
##     year = {2024},
##     note = {R package version 1.0.0},
##     url = {https://CRAN.R-project.org/package=rnaturalearthdata},
##   }

Citations for FoodMicrobionet.

Reference DOI
Parente, E., Cocolin, L., De Filippis, F., Zotta, T., Ferrocino, I., O’Sullivan, O., Neviani, E., De Angelis, M., Cotter, P. D., Ercolini, D. 2016. FoodMicrobionet: a database for the visualisation and exploration of food bacterial communities based on network analysis. Int. J. Food Microbiol. 219: 28-37. 10.1016/j.ijfoodmicro.2015.12.001
De Filippis, F., Parente, E., Zotta, T., Ercolini, D. 2018.A comparison of bioinformatic approaches for 16S rRNA gene profiling of food bacterial microbiota. Int. J. Food Microbiol. 265:9-17. 10.1016/j.ijfoodmicro.2017.10.028
Parente, E., De Filippis, F., Ercolini, D., Ricciardi, A., Zotta, T., 2019. Advancing integration of data on food microbiome studies: FoodMicrobionet 3.1, a major upgrade of the FoodMicrobionet database. Int. J. Food Microbiol. 305:108249. 10.1016/j.ijfoodmicro.2019.108249
Parente, E., Zotta, T., Ricciardi, A., 2022. FoodMicrobionet v4: A large, integrated, open and transparent database for food bacterial communities. Int J Food Microbiol 372, 109696. 10.1016/j.ijfoodmicro.2022.109696

  1. by convention in FoodMicrobionet a study must have a unique bioproject accession and a sample a unique biosample accession↩︎